Data Scientist / Machine Learning Engineer

About The Company:
About us: ”Climate Connect Technologies applies Artificial Intelligence (AI) forecasting techniques such as Neural Networks, Support Vector Machines and Gradient Boosting, to a range ofm opportunities within the evolving energy ecosystem. Climate Connect was founded in 2010 by Cambridge University alumni, after rapid growth the young company now has a team of 40+ people spread across 3 main locations: Delhi (India), Pune (India), Amsterdam (NL). We also run a carbon market intelligence platform, CaliforniaCarbon.info, and so have strong business interests and are firmly embedded in the energy networks on the US West Coast.

Website: http://www.climate-connect.com/

Job Location: Pune

Desired Experience: 0-1 Years

Course Specialization: B.E./B.Tech(CS/IT)/BCA/MCA

Target Batch: 2019-2020

Salary: INR 5 LPA - INR 6 LPA
(The final CTC will depend upon candidate's performance in the interview and will be at the company's discretion)

Tentative date of interview: Will be communicated post registration window is closed

Tentative date of joining: Immediate

* The shortlisted candidates will be sent Admit Cards/Call Letters on their registered mail Id, which they will need to, carry on the date of Interview. No candidate will be entertained by the company without the formal intimation from Aspiring Minds.

Job Description:
Responsibilities:
- Translate high-level problems and key objectives into granular modelrequirements.
- Define acceptance criteria that are well structured, detailed, andcomprehensive.
- Developingandtestingelectricityandcarbonallowancepriceforecastingalgorithms usinglargedatasetssuchasload,weather,historical,grid,forwardmarketsetc.
- Developingandtestingalgorithmsusingourpriceforecasts,andcustomers\'energy portfolio.
- Leadingsoftwareengineeringteamindeployingthedevelopedmodelstailoredto specific customerneeds.
- Participatinginthesoftwaredevelopmentprocess,testing,anddebuggingrequiredto support the deployedmodels.
- Ensuretrackingofappropriateevents/metrics,sothatmonitoringistimelyandrigorous.
- Drivetheresponsetothediscoveryofregressionsorfailures,byundertakingvarious exercises (e.g. debugging, RCA, etc.) asneeded
- Stayup-to-dateonevolutionsinbestpractice,tooling,andstrategiesintheMLspace.

Requirements:
- Advanced knowledge ofPython.
- ExperienceinworkingwithlibrarieslikeNumpy,Pandas,sk-learn,tensorflow2.0, pytorch, keras, xgboost, autograd, plotly, Jupyteretc.
- Knowledge of ML algorithms like clustering, SVMs, NNs, XGBoostetc.
- Experienceworkingwithlargedatabasestoaccess,manipulateandprocessdata. Knowledge of MySQL, MongoDB, ElasticSearch or other nosql database implementations.
- ComfortablewiththeconceptofAPIsandJSON-REST.Abletoaccessandworkwith various dataAPIs.
- Comfortablewithawidesetofmachinelearningapproachesanddesigningthefeatures and data processing to actually make themwork.
- Demonstrateabilitytotransformtheoreticalknowledgetopractical,real-worldsituations.
- Beresults-oriented,abletomeettightdeadlinesandproduceclearandconcise feedback/reports to seniormanagement.
- Provenabilitytoworkonmultiplecomplexandcompetingbusinessobjectivesinahighly fluid and dynamicenvironment.
- ExcellentEnglishcommunicationskills;theabilitytoconveyyourmessagetoteam members and otherstakeholders
- Flairforusingtherightmetricstomeasuremodelperformance,quality,andreliability.
- Abilitytothinkcriticallyandconceptuallyabouttheroleofeachfeature/developmentunit in the achievement of the product’sgoals
- Aptitudeforandinterestintakingownershipoftheroadmap(andachievements)ofa product
- Willingnesstoembracethestart-upmindset:Ambition,adaptability,andaget-it-done approach
- Experience with consumer marketing, or utilities markets is aplus.
- Experience with semi-remote or fully distributed teams is aplus.
- Good to have knowledge of backend technologies Message Queues,IPC.
- GoodtohaveknowledgeofworkingwithcloudproviderslikeAWSandDigitalOcean.

Structure of take-home assignment:
Part I - Data Science problem on model building. Code submission via email.
Part II - Report on approach taken, final model description and performance metrics.

Interview Process:
- Assignement Round
- Technical Round - 1
- Technical Round - 2
- HR Round